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Latent growth models matched to research questions to answer questions about dynamics of change in multiple processes.

ABSTRACT: Given theoretical and methodological advances that propose hypothesis about change in one or multiple processes, analytical methods for longitudinal data have been developed that provide researchers with various options for analyzing change over time. In this paper, we revisited several latent growth curve models that may be considered to answer questions about repeated measures of continuous variables, which may be operationalized as time-varying covariates or outcomes.To illustrate each of the models discussed and how to interpret parameter estimates, we present examples of each method discussed using cognitive and blood pressure measures from a longitudinal study of aging, the Origins of Variance in the Old-Old study.Although statistical models are helpful tools to test theoretical hypotheses about the dynamics between multiple processes, the choice of model and its specification will influence results and conclusions made.

SUBMITTER: Muniz-Terrera G

PROVIDER: S-EPMC5325805 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Latent growth models matched to research questions to answer questions about dynamics of change in multiple processes.

Muniz-Terrera Graciela G Robitaille Annie A Kelly Amanda A Johansson Boo B Hofer Scott S Piccinin Andrea A

Journal of clinical epidemiology 20160914

<h4>Objectives</h4>Given theoretical and methodological advances that propose hypothesis about change in one or multiple processes, analytical methods for longitudinal data have been developed that provide researchers with various options for analyzing change over time. In this paper, we revisited several latent growth curve models that may be considered to answer questions about repeated measures of continuous variables, which may be operationalized as time-varying covariates or outcomes.<h4>St ...[more]

PMID: 27639542

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Project description:BackgroundMeta-research can involve manual retrieval and evaluation of research, which is resource intensive. Creation of high throughput methods (e.g., search heuristics, crowdsourcing) has improved feasibility of large meta-research questions, but possibly at the cost of accuracy.ObjectiveTo evaluate the use of double sampling combined with multiple imputation (DS?+?MI) to address meta-research questions, using as an example adherence of PubMed entries to two simple consolidated standards of reporting trials guidelines for titles and abstracts.MethodsFor the DS large sample, we retrieved all PubMed entries satisfying the filters: RCT, human, abstract available, and English language (n?=?322, 107). For the DS subsample, we randomly sampled 500 entries from the large sample. The large sample was evaluated with a lower rigor, higher throughput (RLOTHI) method using search heuristics, while the subsample was evaluated using a higher rigor, lower throughput (RHITLO) human rating method. Multiple imputation of the missing-completely at-random RHITLO data for the large sample was informed by: RHITLO data from the subsample; RLOTHI data from the large sample; whether a study was an RCT; and country and year of publication.ResultsThe RHITLO and RLOTHI methods in the subsample largely agreed (phi coefficients: title?=?1.00, abstract?=?0.92). Compliance with abstract and title criteria has increased over time, with non-US countries improving more rapidly. DS?+?MI logistic regression estimates were more precise than subsample estimates (e.g., 95% CI for change in title and abstract compliance by year: subsample RHITLO 1.050-1.174 vs. DS?+?MI 1.082-1.151). As evidence of improved accuracy, DS?+?MI coefficient estimates were closer to RHITLO than the large sample RLOTHI.ConclusionOur results support our hypothesis that DS?+?MI would result in improved precision and accuracy. This method is flexible and may provide a practical way to examine large corpora of literature.

Project description:BACKGROUND: The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions. RESULTS: A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions. CONCLUSIONS: By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery.

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Latent growth models matched to research questions to answer questions about dynamics of change in multiple processes.

Publications

Latent growth models matched to research questions to answer questions about dynamics of change in multiple processes.

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